Known systems for identification of a user include the use of personal behavior. Information about the user's physiology may be collected, including flight time between keys, key dwell duration, as well as data relating to geographical location, IP address, and other information indicative of the hardware, software and communication protocols through which the system, device or smartphone is accessed. Notably, known systems collect such data in tables, which are periodically updated, thereby refining the dataset and improving the accuracy of identification with successive iterations.
With the growth of cloud computing and other high user count systems, which could be in the hundreds of millions or billions using systems from companies such as Facebook, Google and Twitter, storing a statistically significant sample of logins for comparison when multiplied by the increased number of users results in huge drains on processing resources, related to CPU, memory, bus circuit board speed, and data storage.
Enrollment of new users into a system using keystroke or touch based mobile biometrics requires training a limited number of initial entries, to establish a baseline, which can result in a user profile that expects a very narrow range of mathematical inputs after the initial training sequence is complete and then requires additional logins to “normalize” the baseline mathematics of the scoring. While additional enrollment entries produce improved baseline accuracy, it has been found that excessively long enrollments are undesirable.
What is needed, therefore, are techniques for efficiently confirming identity of users logging into a system.